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prompt-review

This skill should be used when the user asks to "review the prompt", "audit the system prompt", "check prompt quality", "inspect what the LLM sees", "debug prompt issues", or "find prompt engineering problems". Pulls the live rendered prompt via the API, explains how it's composed, and reviews it for issues.

69

Quality

87%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

78%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A highly actionable, domain-dense skill body with executable commands, an explicit review checklist, and a fix-location table. Its main structural weakness is that SKILL.md doubles as a reference manual: the layer-architecture and directory-layout material belongs in a separate reference file, and the workflow lacks an explicit capture-verification checkpoint.

Suggestions

Move the "How Spacebot Composes Prompts" layer-by-layer section and the directory-layout tree into a references/ file (e.g. references/prompt-architecture.md), keeping SKILL.md to the procedure plus a short layer summary with clear pointers — this would lift both progressive_disclosure and conciseness.

Add an explicit validation checkpoint after Step 2 (e.g. "Confirm /tmp/prompt_inspect.md is non-empty and total_chars is plausible before proceeding") to close the workflow's implicit-verification gap.

Collapse the 18-item rendering-order list or merge it into the layer descriptions to remove the duplicated enumeration and trim token spend.

DimensionReasoningScore

Conciseness

Nearly every token covers proprietary Spacebot specifics Claude cannot know (layer architecture, paths, API endpoints, template names) and it assumes Claude's competence throughout. Minor over-length remains: the 18-item rendering-order list substantially restates the per-layer sections, and the directory tree includes entries not needed for the review task — so anchor 4 rather than the lean-every-token-earns-its-place of 5.

4 / 5

Actionability

Fully executable guidance: copy-paste-ready curl/jq commands ("curl -s http://localhost:19898/api/channels/inspect?channel_id=... | jq -r '.system_prompt'"), exact file paths and template names to read, a concrete review checklist, and a problem-origin-to-fix-location table. Not a 4: the commands and checklists cover the common cases end to end with no gaps.

5 / 5

Workflow Clarity

A clear five-step procedure (list channels → pull prompt → read sources → review against checklist → structured report) with fallbacks for failure modes (uninspectable channel → read templates directly; hosted vs local path variants). Falls short of 5 because validation of the fetch itself is implicit — there is no explicit checkpoint confirming the prompt was captured (e.g., check the output file is non-empty or total_chars) before proceeding to analysis.

4 / 5

Progressive Disclosure

The body is well-sectioned with clear headers and no nested references, but roughly half of it — the six-layer composition architecture (~100 lines) and the full directory-layout tree — is reference material inlined in SKILL.md that would serve better in a references/ file the procedure points to. That matches anchor 3 ("content that should be separate is inline") rather than 4, since the split is substantial, not a minor organization gap; there are no bundle files to score against.

3 / 5

Total

16

/

20

Passed

Description

91%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong description: third-person voice, an explicit use-when clause with six natural trigger phrases, and three concrete capability statements. Its only weaknesses are a slightly generic final clause ("reviews it for issues") and minor overlap risk with broader review-type skills.

DimensionReasoningScore

Specificity

"Pulls the live rendered prompt via the API, explains how it's composed, and reviews it for issues" lists several specific concrete actions (API pull, composition explanation, issue review). Falls short of 5 because "reviews it for issues" is generic and the issue categories are not enumerated; clearly above 3 since it goes well beyond naming a domain with 1-2 actions.

4 / 5

Completeness

Explicitly answers both questions: what ("Pulls the live rendered prompt via the API, explains how it's composed, and reviews it for issues") and when ("This skill should be used when the user asks to...") with concrete trigger phrases. This is the anchor-5 pattern of a clear what plus an explicit use-when clause, not the weaker implied-when of a 4.

5 / 5

Trigger Term Quality

Six natural quoted trigger phrases — "review the prompt", "audit the system prompt", "check prompt quality", "inspect what the LLM sees", "debug prompt issues", "find prompt engineering problems" — cover the natural synonym space (review/audit/check/inspect/debug). This matches the comprehensive-with-synonyms anchor; no natural variation is obviously missing.

5 / 5

Distinctiveness Conflict Risk

The trigger set carves out a distinct prompt-audit niche, but phrases like "review the prompt" and "check prompt quality" could overlap with generic code-review or quality-check skills in a shared library. Mostly distinct with minor overlap risk (anchor 4) rather than the minimal-conflict clarity of anchor 5.

4 / 5

Total

18

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
spacedriveapp/spacebot
Reviewed

Table of Contents

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